Browse Topic: Internet
Communicating when traumatic brain injury, stroke, or disease has made speech impossible can be daunting. But specialized eye-tracking technology uses eye movement to enable people living with disabilities to connect one-on-one, over the phone, or via the internet.
Space lasers are transforming the world. Not the far-off future of science fiction, but the universe of how data and communications flow today - everywhere from deep space missions to countless applications here on earth, including consumer internet services, military operations, and banking transactions. Lasers can transmit vast amounts of data over great distances at the speed of light, 100 times faster than previously possible in space. The narrowness of the light beams makes laser communication remarkably efficient. The highly focused light is aimed at the receiver, resulting in minimal beam divergence and signal loss and allowing for reduced power consumption.
The presence of a slung-load during the flight of a quadrotor generates swing effects that can greatly influence the dynamics of the quadrotor. These effects have the potential to threaten the stability of the system’s attitude. This study presents a disturbance compensation strategy that is designed based on the utilization of an adaptive harmonic extended state observer (AHESO) in order to solve this problem and achieve precise attitude control. To derive the aforementioned algorithm, a comprehensive mathematical model for the quadrotor-slung-load system is built. The periodic features of disturbance are derived by considering the movement of the slung-load. Subsequently, by taking the periodic features of the disturbances into account, the AHESO for accurate disturbance estimation is designed. In this observer, an online frequency estimator for the harmonic disturbances is introduced. Lyapunov theory is introduced to examine the stability of the AHESO. In addition, backstepping-based approach is employed to design an attitude controller for accurate attitude tracking of the quadrotor. Analysis are conducted regarding of the entire stability of the quadrotor-slung-load system. In the end, the effectiveness of the designed method is verified by comparative simulations.
The concerns surrounding AV adoption encompass the data protection factor. An online survey was conducted to gain insights into this concern, targeting UAE residents with knowledge about Autonomous Vehicle (AV) technology. The collected data were subjected to statistical analysis to provide valuable information for the UAE government and private sectors. To achieve this goal, we conducted a statistical analysis of the collected data, which resulted in further insights regarding the obstacles impeding the adoption of AV technologies in the United Arab Emirates. This analysis further quantifies the factors that contributed to UAE public concerns. We also examined user group evaluations in terms of their propensity to employ the technology in the future.
Imagine a team of humans and robots working together to process online orders — real-life workers strategically positioned among their automated coworkers who are moving intelligently back and forth in a warehouse space, picking items for shipping to the customer. This could become a reality sooner than later, thanks to researchers at the University of Missouri, who are working to speed up the online delivery process by developing a software model designed to make “transport” robots smarter.
Drawing inspiration from how spiders spin silk to make webs, a team of researchers from the National University of Singapore, together with international collaborators, has developed an innovative method of producing soft fibers that possess three key properties (strong, stretchable, and electrically conductive), and at the same time can be easily reused to produce new fibers.
Over the last three decades, storage on the internet increased by over 40,000 percent from 15.8 exabytes in 1993 to 6.8 zettabytes in 2020. While it is difficult to determine the exact number, as of February 2022, the size of the internet is estimated to be about 21 zettabytes and is doubling every two years. If we assume the average personal computer (PC) has a hard drive of one terabyte, 21 zettabytes is equivalent to 21 billion PCs, essentially three PCs for every person in the world. While a lot of this data is personal data, a large portion of it is considered publicly available information (PAI) and can be utilized by any internet user or organization.
People are exposed to disproportionately high concentrations of air pollutants daily on and near the road during their ride, walk, and residency. Currently air quality monitoring is performed mostly at background locations far away from the road and distinctive point, line, and area emission sources, failing to report concentrations on the road. This study presents an innovative air quality monitoring method using a network of vehicles equipped with air quality sensors (AQSs). Vehicles are connected to internet through either cell link or apps used by drivers. A cloud server collects the data and updates air quality concentration maps every 10 minutes. The study presents very high temporal and spatial resolution maps at a fraction of the cost of previous studies. These high spatiotemporal resolution air quality maps can be used to reduce public exposure in many ways including but not limited to cabin air flap door control, eco-driving route for navigation, and daily exposure monitoring. The method has a potential to be deployed worldwide.
The way in which businesses, enterprises, industry leaders, and consumers utilize technology for everyday tasks is set to undergo one of the most drastic evolutions ever. Just a few short years ago it was nearly impossible to think any other technology could have a greater impact than networked computers, the Internet, or even mobile computing, but now technologies like artificial intelligence (AI), Internet of Things (IoT) and AR/VR are being hyped more than ever.
The Grainger College of Engineering at the University of Illinois at Urbana–Champaign was established in 1868. The department has historically spearheaded worldwide innovation in technology with inventions such as the transistor, the integrated circuit, the LED, the first web browsers (Mosaic and Netscape), and (JavaScript) — all produced by students, faculty, or alumni of the college.
The fast growth of electric vehicles has resulted in the widespread application of lithium-ion batteries. Recognized as a critical problem, the accurate estimation of the battery state has drawn much attention. Meanwhile, the continuous progress of the Internet of vehicles technology promotes the algorithm on cloud platform. However, the state of charge and state of health estimation based on on-board battery management system present deficiency such as data loss, noise interference and inconsistent sampling interval through the transmission. Thus, this article developed a multi-scale co-estimation method on the SOC and SOH with consideration of the dataset quality. Firstly, a Thevenin model for SOC estimation is constructed and parameters are identified by least square method. It is noteworthy that the frequency of SOC and SOH updates different time scales. To achieve the co-estimation on the both state and health, the extended Kalman filter algorithm is used twice. The dual extended Kalman filter is applied to operate at different time scales depending on the frequency of state and health updates. Finally, the impact of data loss, repetition and time scale is discussed where the dataset is incomplete through transfer protocol. Simulation confirms that this algorithm can still achieve maximum deviation of 5% for SOC estimation under sampling frequency, and the error can be limited within 6% for capacity estimation. The article provides an SOC and SOH estimation technique for the cloud platform.
The purpose of this SAE Aerospace Standard (AS) is to standardize the basic design, performance, and testing requirements for “Cargo Stoppers” cargo tie-down accessories to be used in conjunction with approved restraint straps meeting AS5385C (TSO C-172) requirements.
Cloud computing technologies are embodied with automotive sector copiously. It aids in using data and computing services to manage information, communication, and computing, through Internet-based apps and online digital services. A cloud computing-based framework is suitable for developing and deploying simulation models to study, analyse and optimise the vehicle performance. The framework proves functional in collecting vehicle data, processing and then using them for datadriven or model-based development to deliver a complete software solution. Server-less cloud computing technologies with storage and function triggers form the architecture. The paper outlines a data-driven model of a Three-Way Catalyst (TWC) to test the cloud framework as an end-to-end solution. The model estimates a metric to quantify the oxygen storage capacity of the TWC over the air. This metric is an online adaptive gain, estimated through system diagnosis using the Recursive Least Squares method. This is followed by a Decision Tree Classification algorithm to classify these metrics according to their useful life. Thus, realising TWC health diagnostics.
A CAN transceiver with built-in security functions can avoid the complexity of end-to-end security solutions that are especially hard to implement on CVs. Commercial road vehicles are the backbone of the modern consumer economy. Almost any business from construction, to energy, to online retail at some point relies on the delivery of goods by commercial vehicles, which in turn are becoming increasingly connected both to the external world and to each other via telematics. This enables CV owners to optimize and manage their fleets via platooning for safety and efficiency improvements as well as cost and fuel-consumption reduction to meet the increasingly stringent CO2 emissions requirements necessitated by climate change. However, the increased connectivity brings with it an increase in cyberattack surfaces and CV fleets are prime targets for cybercrime due to the high value of the cargo they carry, and their importance to large businesses and the greater economy. While CV manufacturers are familiar with and prepared for the risk of physical attacks - typically carried out on one vehicle, such as odometer manipulation or theft - they may risk being caught by surprise at the scale and impact of what is possible with remote cyberattacks. Hackers can exploit a vehicle's wireless network or internet connection to gain entry into the vehicle's communication network and compromise security to access a vehicle's CAN (Controller Area Network) and take over remote management of the vehicle while it is in motion.
The USC Viterbi School of Engineering (formerly the USC School of Engineering) is the engineering school of the University of Southern California. The school is among the nation’s highest in volume of research activity. Its research centers have played a major role in development of multiple technologies, including early development of the internet.
Researchers have demonstrated a silicon-based optical communication link that combines two multiplexing technologies to create 40 optical data channels that can simultaneously move data. The new chip-scale optical interconnect can transmit about 400 GB of data per second — the equivalent of about 100,000 streaming movies. This could improve data-intensive internet applications from video streaming services to high-capacity transactions for the stock market.
To further explore the potential of fuel economy for hybrid electric vehicle (HEV), a shared-control-based energy management strategy (SCEMS) with four modules of the human-vehicle closed-loop system, reference power calculation, driver power distribution, and shared control strategy is proposed. The SCEMS possesses three innovations. Firstly, the rational driver’s power demand is considered to achieve optimal fuel consumption. Secondly, a dimensionality reduction strategy of two-dimension DP algorithm is proposed for online application. Finally, based on the shared control and the intelligent traffic system (ITS) a game mechanism between driver and controller is constructed to adapt to different driving styles and road conditions. In the human-vehicle closed-loop system, a model is built, combining the driver model with a longitudinal dynamic model, to optimize the power demand and power distribution. In the reference power calculation, the dynamic programming (DP) algorithm is utilized to produce the optimal power of a future forward road segment based on the ITS. The time complexity of DP algorithm is reduced by a state of charge (SOC) table looked up online derived from neural network and road condition identification. In the driver power distribution, the original demand power is assigned to the engine and motor. In the shared control strategy, two condition description equations are respectively constructed to indicate the fuel consumption rate of the engine and the efficiency of the motor, and then two adjustment curves are fitted to regulate the proportion of driver and controller to improve the power-wasting and ineffective behaviors. The problem of driving style and emergency road condition adaptation is settled depending on the cumulative-error-based weight adjustment strategy.
This SAE Recommended Practice provides a Glossary of Terms commonly used to describe Seat Belt Restraint Systems Hardware and their function. These terms are currently defined in various SAE Recommended Practices but are sometimes inconsistent. It is intended for this document to supersede the definitions found in separate SAE Recommended Practices.
Security in encrypted communication is a top priority because of our highly connected and mobile society’s increasing reliance on the internet. Engineers at Department of Electrical Engineering and Computer Science and the Research Laboratory of Electronics, MIT, have developed a new protocol for high-speed communication between two parties with security vouchsafed by the laws of quantum physics. The protocol can also be used to distribute cryptographic keys, as in quantum key distribution (QKD) at much higher secure key rates than existing QKD methods.
Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions. SVM algorithm with Quadratic Kernel function trained using power signature features predicts weld conditions considered in this study with an accuracy of 99%.
There’s no doubt that Industrial Ethernet (IE) is paving the way for the automated factory of the future. IE is the backbone of modern industrial communications between devices on the plant floor, the enterprise information network, and the cloud services that companies increasingly rely upon to build and grow their business. At the macro level, IE is as essential to automation control as the internet is to e-commerce.
Operational Technology (OT) networks are historically “air-gapped” from the rest of the networked world. But pressure is mounting for manufacturers to leverage real-time data exchanges from outside their controlled environments to increase productivity and efficiency. One major concern for manufacturers is how safe and secure it is to open the OT network’s doors to the internet.
Whenever you buy something online, your customer data is automatically updated and stored on thousands of virtual machines in the cloud. But up to now, there has been no way to guarantee that a software system is secure from bugs, hackers, and vulnerabilities. Now, researchers have developed SeKVM, the first system that guarantees — through a mathematical proof — the security of virtual machines in the cloud.
Based on the information of the bend ahead which obtained through V2X, high-definition map (HD Map), vehicle positioning or other technologies, the velocity planning and online gear decision method are explored with the actual driving state of a P2 configuration plug-in hybrid electric car when it crosses the bend, to achieve better energy economy while ensuring the driving safety. In this paper, firstly, according to the basic characteristics of the hybrid car, a simplified simulation model is built in MATLAB / Simulink to provide a verification platform for the research. Subsequently, the calculation method of safety speed in bends is established by considering the driver factor and the critical conditions when the vehicle rolls over, sideslip or oversteer. Then, according to the information of the bend ahead, the safety speed, and the general situation when vehicles cross the bend, the whole process is divided into three stages of deceleration before entering the bend, uniform velocity in the bend, and acceleration after leaving the bend. The economic velocity planning is carried out respectively in each stage and the effectiveness of the results is verified by simulation. Finally, according to the planned economic velocity, the online gear decision method based on Model Predictive Control (MPC) is proposed, and the simulation is completed in a self-defined bend scene. The results show that compared with the traditional gear shift map, the MPC gear decision method can reduce the equivalent fuel consumption by 27.64% and 22.63% separately when the initial SOC is 35% and 45%.
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